A Rule-Based Approach to Aspect Extraction from Product Reviews
By Soujanya Poria, Erik Cambria, Lun-Wei Ku, Chen Gui, Alexander Gelbukh. SocialNLP@COLING, 2014
301 citations recorded by the source corpus.
Sentiment analysis is a rapidly growing research field that has attracted both academia and industry because of the challenging research problems it poses and the potential benefits it can provide in many real life applications. Aspect-based opinion mining, in particular, is one of the fundamental challenges within this research field. In this work, we aim to solve the problem of aspect extraction from product reviews by proposing a novel rule-based approach that exploits common-sense knowledge and sentence dependency trees to detect both explicit and implicit aspects. Two popular review datasets were used for evaluating the system against state-of-the-art aspect extraction techniques, obtaining higher detection accuracy for both datasets.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/7dec03bcf97f2440e4178f574aa5dcc8b7bee7e8
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In Artificial Intelligence
Citations in the corpus
- Lifelong-RL: Lifelong Relaxation Labeling for Separating Entities and Aspects in Opinion Targets (2016, background): “…et al., 2009; Lin and He, 2009; Zhang et al., 2010; Mei et al., 2007; Li et al., 2010; Brody and Elhadad, 2010; Wang et al., 2010; Mukherjee and Liu, 2012; Fang and Huang, 2012; Zhou et al., 2013; Liu et al., 2013; Poria et al., 2014), we are not aware of any attempt to solve the proposed problem.”
- Aspect-Based Financial Sentiment Analysis using Deep Learning (2018, background): “Rule based approach have shown good results in aspect extraction[10] but eventually deep learning models out performed conventional models in this task too[14].”